A sea ice remote sensing identification method based on ultraviolet reflection difference

Abstract

The present application relates to a kind of sea ice remote sensing identification method based on ultraviolet reflection difference, utilize the unique optical signal difference of sea ice and cloud and other confusion target in ultraviolet band reflection, realize the quick and accurate identification of sea ice in multi-source remote sensing image, and the sea ice density and sea ice coverage area estimation of sea ice image after cloud mask.This application can provide timely and efficient guidance information, the multi-source remote sensing image used only needs to be radiometrically calibrated and atmosphere corrected, obtain optical band apparent reflectivity and thermal infrared band brightness temperature image, can accurately identify and extract sea ice;Compared with traditional optical observation means, the present application is suitable for the situation that optical remote sensing image is easily disturbed by cloud cover, and cloud interference information can be identified and removed at the same time;The method can meet the demand of sea ice operational monitoring, improve the work efficiency of sea ice observation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of satellite remote sensing polar sea ice monitoring, and particularly relates to a sea ice remote sensing identification method based on ultraviolet reflection difference. BACKGROUND

[0002] Sea ice is an important object of marine environment monitoring, and its range and thickness change greatly, which can cover about 10% of the Earth's oceans. Sea ice plays an important role in the global system, and has been integrated into climate models, which can have a profound impact on the polar and global climate, oceans and ecosystems. In addition, sea ice also affects human scientific research and production activities in the polar region, and is an important factor threatening human ship transportation and navigation in the polar sea area, as well as the safety of offshore oil platform exploitation. The research on sea ice changes is mainly carried out through its area, coverage, density, thickness, surface temperature and other aspects.

[0003] The development of satellite remote sensing technology opens up a new way for large-scale sea ice monitoring, which can cover a wide sea area in a short time and obtain key information such as the distribution range and thickness of sea ice. Among them, satellite optical remote sensing provides good technical support for the dynamic monitoring of sea ice. For example, China's Haiyang-1C / D (HY-1C / D) and the United States' Moderate-resolution Imaging Spectroradiometer (MODIS) have been widely used in the observation and research of polar sea ice. Optical remote sensing data can provide multi-band or even high-spectral information, and based on the reflectivity difference between sea ice and background seawater, the band threshold method can effectively identify sea ice. And for some small range or specific area of sea ice monitoring, using high spatial resolution optical remote sensing data can achieve more detailed observation. In the process of sea ice observation, sea ice concentration (SIC) and sea ice extent are important parameters to describe sea ice, which are defined as the proportion of sea ice coverage per unit area and the sum of the area covered by sea ice in a certain area respectively, and the sea ice extent can be calculated from the sea ice concentration.

[0004] Sea ice remote sensing monitoring is carried out by using optical remote sensing data to provide sea ice density and sea ice range. However, due to the differences in the band setting, signal-to-noise ratio and other differences of different optical satellite payloads, the current sea ice optical remote sensing monitoring is mainly disturbed by clouds, both of which show high reflection characteristics in optical remote sensing images. The new generation of multi-band remote sensing image data carries ultraviolet band, and sea ice, clouds, seawater and the like have unique scattering characteristics in their respective reflection spectra, which are in the form of peaks and valleys, and the position and value thereof can be used for removing interference information and extracting sea ice information. Therefore, accurate identification and extraction of sea ice and key information have important practical significance for sea ice monitoring. SUMMARY

[0005] The technical problem to be solved by the present application is that, in view of the characteristics that sea ice optical remote sensing identification is easily disturbed by confusing targets such as clouds, classification and identification are carried out based on the reflection difference of sea ice and clouds on incident light in the ultraviolet band, and sea ice density and sea ice coverage are estimated, which has important significance for sea ice monitoring.

[0006] In order to solve the above technical problems, the present application provides a sea ice remote sensing identification method based on ultraviolet reflection difference, comprising the following steps:

[0007] Step 1, data preprocessing

[0008] The multi-source remote sensing image data is preprocessed, and the optical band data is converted into apparent reflectivity ρ TOA Data, the optical band includes ultraviolet band, visible light band and near-infrared band, and the thermal infrared band data is converted into brightness temperature data BT;

[0009] Step 2, interested area delineation

[0010] The image data preprocessed by step 1 is used to cut out the interested area containing sea ice by using a vector file;

[0011] Step 3, spectral information enhancement

[0012] The background water spectrum difference value calculation is carried out on the apparent reflectivity data ρ TOA , and the background seawater spectrum is subtracted pixel by pixel to weaken the influence of background water color; the background seawater difference value data is normalized to obtain normalized difference reflectivity data ρ' TOA ;

[0013] Step 4, characteristic band detection

[0014] The multiple pieces of typical spectral data of the typical targets are selected as the training samples, the typical spectral data refers to the characteristic spectral data of the spectra of the specified targets obtained through the above steps 1-3, the average value of the training sample characteristic spectral data of each target is taken, and the contrast C between the spectral characteristic data is calculated according to the following formula

[0015]

[0016] In the formula, ρ TOA,UV / RED is the apparent reflectivity value of the characteristic spectral data of the typical target in the ultraviolet band or the red light band, ρ TOA,VNIR is the apparent reflectivity value of the characteristic spectral data of the typical target in the visible light-near infrared band; the typical target includes sea ice, cirrus, cumulus and seawater; the two band combinations with the maximum contrast C are selected for sea ice extraction based on ultraviolet reflection difference and confusion target elimination, and the finally selected band combinations are: the ultraviolet band and the red light band combination, and the ultraviolet band and the near infrared band combination; step 5, sea ice identification based on ultraviolet band reflection difference is performed on the image, and the specific steps are as follows:

[0017] 1), for the near infrared band gray scale image ρ TOA in the apparent reflectivity data ρ TOA,NIR , threshold value segmentation is performed using a threshold value ε1 to obtain background seawater pixels and other to-be-divided pixels, and the specific formula is as follows:

[0018]

[0019] The pixels with 1 in the Water_mask image represent seawater, and the pixels with 0 represent non-seawater;

[0020] 2), the Water_mask image is used as a mask to operate on the normalized difference reflectivity data ρ' TOA to eliminate seawater pixels, and for the normalized difference reflectivity data ρ' TOA after eliminating the seawater pixels, the corresponding index is constructed using the ultraviolet band, the green light band and the red light band, and the brightness temperature image data BT is obtained, threshold value segmentation is performed using threshold values ε2, ε3 and ε4 to obtain cirrus pixels, and the specific formula is as follows:

[0021]

[0022] In the formula, T1=ρ' TOA,RED -ρ' TOA,GREEN , ρ' TOA,UV is the normalized difference reflectivity data of the ultraviolet band, and the pixels with 1 in the Cirrus_mask image represent cirrus, and the pixels with 0 represent non-cirrus;

[0023] 3) using the Cirrus_mask image as a mask to the normalized difference reflectance data ρ' TOA Operation to eliminate sea water and cirrus pixels, for the normalized difference reflectance data ρ' TOA , the red light band and the near infrared band are used to construct the corresponding index, and the brightness temperature image data BT is obtained, threshold value ε4 and threshold value ε5 are used for threshold segmentation, to obtain cumulus pixels and sea ice pixels, the specific formula is as follows:

[0024]

[0025] Wherein, T2=ρ' TOA,NIR / ρ' TOA,RED , ρ' TOA,NIR is the near infrared band normalized difference reflectance data, ρ' TOA,RED is the red light band normalized difference reflectance data, the pixel of 1 in the Cumulus_mask image indicates cumulus, and the pixel of 0 indicates non-cumulus, that is, the sea ice pixel extraction result.

[0026] Based on the sea ice pixel extraction result in step 5, the highest frequency of apparent reflectivity ρ TOA value is obtained, which is used as the apparent reflectivity value of pure sea ice, combined with the apparent reflectivity value of seawater pixel, the sea ice concentration SIC (Sea ice concentration) is calculated; according to the area of sea ice pixel, combined with the sea ice concentration, the sea ice coverage area S ice (Sea ice extent) is calculated.

[0027] Using the unique optical signal difference of sea ice and cloud confusion targets in ultraviolet band reflection, the fast and accurate identification of sea ice in multi-source remote sensing image is realized, and the sea ice density and sea ice coverage area of sea ice image after cloud mask are estimated. The present application can provide timely and efficient guidance information, the multi-source remote sensing image used only needs to be radiometrically calibrated and atmospherically corrected to obtain optical band apparent reflectivity and thermal infrared band brightness temperature image, so that the sea ice can be accurately identified and extracted; compared with the traditional optical observation means, the present application is suitable for the case that optical remote sensing image is easily disturbed by cloud cover, and the cloud interference information can be identified and eliminated at the same time (cloud is similar to sea ice in visual characteristics and spectral morphological characteristics, examples are given in the specification of the present application); the method can meet the demand of sea ice business monitoring, and improve the work efficiency of sea ice observation. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 It is the flowchart of the present application.

[0029] Figure 2The HY-1C / D true color composite remote sensing image of sea ice, clouds and background seawater in the region of interest.

[0030] Figure 3-a The 355 nm ultraviolet band apparent reflectance image of the region of interest.

[0031] Figure 3-b The 1080 nm thermal infrared band brightness temperature image of the region of interest.

[0032] Figure 4 The apparent reflectance, background seawater difference, and normalized difference reflectance spectrum of sea ice, clouds and background seawater.

[0033] Figure 5 The contrast index diagram of the training-recognition of the application.

[0034] Figure 6 The target detection classification threshold setting diagram of the application.

[0035] Figure 7 The sea ice and cloud recognition result diagram of the application.

[0036] Figure 8 The sea ice density and sea ice coverage area estimation result diagram of the application. DETAILED DESCRIPTION

[0037] The preferred embodiments of the application will be described in detail below with reference to the accompanying drawings, so that the advantages and features of the application can be more easily understood by those skilled in the art, and the protection scope of the application can be more clearly defined.

[0038] The present embodiment is applied to HY-1C / D satellite multi-band remote sensing image data of several scenes from June to August 2022, covering the Chukchi Sea and Beaufort Sea in the Arctic, and the image contains 10 optical bands of 355 nm, 385 nm, 412 nm, 443 nm, 490 nm, 520 nm, 565 nm, 670 nm, 750 nm, 865 nm, and 2 thermal infrared bands of 1080 nm and 1200 nm, with a pixel spatial resolution of 1100 m.

[0039] As shown in Figure 1 , it is a flowchart of the sea ice remote sensing recognition and density estimation method based on ultraviolet reflectance difference of the embodiment of the application, comprising the following steps:

[0040] Step 1, data preprocessing

[0041] The HY-1C / D satellite multi-band remote sensing image data is preprocessed, including geometric correction, radiation calibration, atmospheric correction and smoothing filtering, and the optical band data is converted into apparent reflectance TOAData, convert the thermal infrared band data to brightness temperature BT data.

[0042] Step 2, the interested area is circled

[0043] The interested area selected in this embodiment is Chukchi Sea and Beaufort Sea in the North Pole. For the sea ice distribution area, the interested area containing sea ice is circled on the image after the pre-treatment in step 1 by using the vector file of the study area.

[0044] In this embodiment, Figure 2 , 3 are respectively the HY-1C / D satellite true color composite images after the pre-treatment in steps 1 and 2 of 6 interested areas, 355 nm ultraviolet band apparent reflectivity ρ TOA gray images and 1080 nm thermal infrared band brightness temperature BT gray images. Sea ice and cloud have similar visual features in the image, which causes a certain challenge to the identification and extraction of sea ice.

[0045] Step 3, spectral information enhancement

[0046] For the study target (sea ice), the typical interference target (cirrus cloud, cumulus cloud) and the background sea water, the apparent reflectivity ρ TOA is detected. Figure 4 -a to Figure 4 -d, the spectral set is constructed.

[0047] The apparent reflectivity ρ TOA of the target is subtracted from the background sea water spectrum, and the background sea water difference spectrum as shown in Figure 4 -e is obtained; the difference spectrum is normalized, and the result is shown in Figure 4 -f. The above steps can effectively eliminate the reflectivity value difference caused by the difference of regions and radiation energy, and improve the uniformity of the same target spectrum form and the difference of different target spectrum forms.

[0048] The apparent reflectivity ρ TOA of the target is subtracted from the background sea water value ρ TOA,seawater , and then normalized to obtain the normalized difference reflectivity data ρ' TOA,i , so as to achieve the effect of target enhancement:

[0049] a i = ρ TOA,i - ρ TOA,seawater i = 1, 2, 3, …, 8

[0050] ρ' TOA,i = (a i -a i,min ) / (a i,max -ai,min )

[0051] In the formula, i is the band number of the optical band, a i The background seawater difference in the i-band is a. i,max and a i,min These represent the maximum and minimum values ​​in the i-band difference spectral curve, respectively, and finally, the normalized difference reflectance data ρ' of the i-band is obtained. TOA,i .

[0052] like Figure 4 As shown, the original ρ of sea ice, cirrus clouds, and cumulus clouds TOA With similar spectral morphologies, the above processing steps can increase the differences in spectral characteristics between different targets and effectively distinguish them.

[0053] Step 4: Feature Band Detection

[0054] In this embodiment, all targets except sea ice are considered as interference and background targets. The purpose of feature band detection is to suppress the signals of other targets while enhancing the sea ice signal. Multiple typical spectral data points of typical targets (sea ice, cirrus clouds, cumulus clouds, and seawater) from step 3 are selected as training samples. The average value of the feature spectral data of the training samples for each target is taken, and the contrast index C is used to quantitatively evaluate the band combination effect. The formula for calculating the contrast index C is as follows:

[0055]

[0056] In the formula, ρ TOA,355 / 670nm ρ represents the apparent reflectance value of a typical target's characteristic spectrum in the 355nm ultraviolet or 670nm red light band (the apparent reflectance of a typical target's characteristic spectrum is the largest across the entire range of these bands). TOA,VNIR The apparent reflectance values ​​of typical target characteristic spectra in the visible-near infrared band (412nm-865nm).

[0057] The calculation results are as follows Figure 5 As shown in -e, the combination of the 355nm ultraviolet band, the 670nm red band, and the 865nm near-infrared band has the highest contrast C. This combination of bands was used to construct a sea ice and cloud classification algorithm based on ultraviolet reflectance differences.

[0058] Step 5: Perform sea ice identification and target classification on the images.

[0059] Sea ice pixel recognition and extraction is based on ultraviolet band reflectance differences. The specific steps are as follows: Figure 6 As shown:

[0060] 1) Background seawater exhibits strong absorption characteristics in the near-infrared band; therefore, for near-infrared grayscale images ρ TOA,865nm Set the threshold ε1 = 0.05 for ρ TOA,865nmThe image is threshold segmented to obtain the sea water pixel extraction result, and the formula is as follows:

[0061]

[0062] The pixel with 1 in the Water_mask image represents sea water, and the pixel with 0 represents non-sea water.

[0063] 2), using the Water_mask image as a mask to the normalized difference reflectivity data ρ' TOA Operation to remove sea water pixels, for the normalized difference reflectivity data ρ' TOA , the image contains sea ice, cirrus and cumulus pixels. From the spectrum in Figure 4 , among them, the cirrus in the 355nm ultraviolet band shows strong scattering characteristics, and has a small reflection peak in the 670nm red light band (which can be represented by the red light green light band difference T1=ρ' TOA,670nm -ρ' TOA,565nm ); In addition, compared with sea ice, high-altitude cirrus has a significantly lower brightness temperature BT in the 1080nm thermal infrared band. Therefore, using the above characteristic bands, setting threshold values ε2=0.25, ε3=0.5, ε4=266.5K for threshold segmentation, the cirrus pixel extraction result is obtained, and the formula is as follows:

[0064]

[0065] The pixel with 1 in the Cirrus_mask image represents cirrus, and the pixel with 0 represents non-cirrus.

[0066] 3), using the Cirrus_mask image as a mask to the normalized difference reflectivity data ρ' TOA Operation to remove sea water and cirrus pixels, for the normalized difference reflectivity data ρ' TOA , the image contains sea ice and cumulus pixels. From the spectrum in Figure 4 , among them, the cumulus in the red light to near infrared band spectrum changes gently (which can be represented by the near infrared red light band ratio T2=ρ' TOA,865nm / ρ' TOA,670nm ); In addition, compared with sea ice, high-altitude cumulus has a significantly lower brightness temperature BT in the 1080nm thermal infrared band. Therefore, using the above characteristic bands, setting threshold values ε4=266.5K, ε5=0.75 for threshold segmentation, the cirrus pixel extraction result is obtained, and the formula is as follows:

[0067]

[0068] The pixel with value 1 in the Cumulus_mask image represents cumulus cloud, and the pixel with value 0 represents non-cumulus cloud, i.e. the sea ice pixel extraction result.

[0069] Step 6, sea ice concentration estimation

[0070] The present embodiment is based on Figure 7 the sea ice extraction result of the multi-band remote sensing image of the HY-1C / D satellite, and estimates the sea ice concentration SIC and the sea ice coverage area S ice . The sea ice concentration is defined as the proportion of the sea ice coverage in the unit pixel area, is mainly applied to the medium and low spatial resolution remote sensing image, and is the basis for estimating the sea ice coverage area. The calculation is based on the 670 nm red light band, and the specific calculation steps are as follows:

[0071]

[0072] In the formula, p is the pixel number, SIC p is the pixel sea ice concentration, p TOA，seawater is the sea water pixel apparent reflectivity threshold value ε5, p TOA，ice is the pure sea ice pixel apparent reflectivity value. The sea water pixel apparent reflectivity p TOA，seawater threshold value is set to 0.05, and when the pixel apparent reflectivity is less than or equal to the threshold value, it is considered to be a pure sea water pixel, and the concentration is 0; the pure sea ice pixel apparent reflectivity p TOA，ice threshold value is obtained by counting the apparent reflectivity value with the highest frequency in the sea ice pixel, and when the pixel reflectivity is greater than or equal to the threshold value, it is considered to be a pure sea ice pixel, and the concentration is 1; when the pixel apparent reflectivity is between the two threshold values, it is considered that the pixel is composed of sea ice and sea water.

[0073] The calculation of the sea ice concentration reflects the proportion of the sea ice in the sea ice and sea water mixed pixel, and to some extent, plays a role in the unmixing of the mixed pixel. In combination with the sea ice coverage area calculated based on the image pixel area, compared with the accumulation of the sea ice pixel area, it is closer to the true value. The sea ice coverage area calculation formula is as follows:

[0074] S ice，p = A x SIC p

[0075] In the formula, A is the image pixel area (1.21 km 2 in the present embodiment), and S ice，p is the sea ice coverage area in the pixel. The sea ice concentration and sea ice coverage area estimation results are shown in Table 1, and the results show that the sea ice pixel area will cause a certain degree of overestimation of the actual area of the sea ice. Figure 8

[0076] ​The above merely illustrates the embodiments of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation, or direct or indirect application in other related technical fields, which are made by using the content of the present application specification and drawings, are also included in the patent protection scope of the present application.

Claims

1. A method for sea ice remote sensing identification based on ultraviolet reflection difference, comprising the following steps: Step 1, data preprocessing Preprocessing multi-source remote sensing image data, converting optical band data into apparent reflectance Step 2, delineation of the region of interest TOA Data, the optical band includes ultraviolet band, visible light band and near infrared band, converting thermal infrared band data into brightness temperature data BT; The image data after step 1 preprocessing is used to cut out the region of interest containing sea ice using vector files; Step 3, spectral information enhancement Step 4, feature band detection Apparent reflectance data Background water spectral difference calculation is performed, and the background seawater spectrum is subtracted pixel by pixel to weaken the influence of the background water color; The background seawater difference data is normalized to obtain normalized difference reflectance data ; Step 5, sea ice identification based on ultraviolet band reflection difference for the image, the specific steps are as follows: A plurality of pieces of typical spectral data of a typical target are selected as the training sample, the typical spectral data being the characteristic spectral data obtained by the above steps 1-3 for the spectrum of the target determined as the specified target, and the training sample characteristic spectral data for each target is averaged, and the contrast between the spectral characteristic data is calculated according to the following formula C C = ； In the formula, is the apparent reflectance value of the typical target feature spectrum in the ultraviolet band or the red light band, is the apparent reflectance value of the typical target feature spectrum in the visible-near infrared band; the typical target includes sea ice, cirrus, cumulus and seawater; the contrast degree is selected as C The two largest band combinations are used for sea ice extraction based on ultraviolet reflectance difference and confusion target elimination, and the finally selected band combination is: the ultraviolet band and the red light band combination, and the ultraviolet band and the near infrared band combination. Water_mask 1) For the apparent reflectance data , the near-infrared band gray image is threshold segmented using threshold Water_mask 1 to obtain background seawater pixels and other pixels to be divided, and the specific formula is as follows: ； Cirrus_mask A pixel with a value of 1 in the image represents seawater, and a pixel with a value of 0 represents non-seawater. 2), use Cirrus_mask The image is a mask for the normalized difference reflectance data Operation to eliminate seawater pixels, normalized difference reflectance data for eliminating seawater pixels , using ultraviolet band, green band, red band to construct corresponding index, at the same time obtaining brightness temperature image data BT, using threshold Cumulus_mask 2, threshold Step 6, sea ice density estimation, specifically as follows: 3 and threshold In step 1, the multi-source remote sensing image includes optical bands of 355-865 nm, thermal infrared bands of 1080 nm and 1200 nm, and the preprocessing includes geometric correction, radiation calibration, atmospheric correction and smoothing filtering. 4 threshold segmentation, get cirrus pixel, specific formula as follows: ； wherein , is the normalized difference reflectance data in the red light band, is the normalized difference reflectance data in the green light band, is the normalized difference reflectance data in the ultraviolet light band, In step 2, when the region of interest is divided, the land is masked according to the map vector data. a pixel with a value of 1 in the image indicates a cirrus cloud, and a pixel with a value of 0 indicates a non-cirrus cloud; 3) using In steps 2 and 3 of step 5, the brightness temperature image data BT selects the thermal infrared band data of 1080 nm. the normalized difference reflectance data as a mask Operation to eliminate sea water and cirrus pixels, normalized difference reflectance data for eliminating sea water and cirrus pixels , using the red band and near-infrared band to construct the corresponding index, while obtaining the brightness temperature image data BT, using threshold In steps 2 and 3 of step 5, the brightness temperature image data BT selects the thermal infrared band data of 1080 nm. 4 and threshold ​ 5 for threshold segmentation to obtain cumulus cloud pixels and sea ice pixels, the specific formula is as follows: ； wherein , is the near-infrared band normalized difference reflectance data, ​ The pixels with value 1 in the image represent cumulus clouds, and the pixels with value 0 represent non-cumulus clouds, i.e. the sea ice pixel extraction result.

2. The sea ice remote sensing identification method based on ultraviolet reflection difference according to claim 1, characterized in that ​ Based on the sea ice pixel extraction result in step 5, the highest frequency of apparent reflectivity value is counted as the apparent reflectivity value of pure sea ice pixel, combined with the apparent reflectivity value of seawater pixel, to calculate the sea ice concentration SIC; according to the area of sea ice pixel, combined with the sea ice concentration, to calculate the sea ice coverage area S ice .​ 3. The method for sea ice remote sensing recognition based on ultraviolet reflection difference according to claim 1, characterized in that: ​ 4. The method for sea ice remote sensing recognition based on ultraviolet reflection difference according to claim 1, characterized in that: ​ 5. The method for sea ice remote sensing recognition based on ultraviolet reflection difference according to claim 1, characterized in that: In step 3, the apparent reflectance is calculated using the following equation Subtract background seawater values Normalization is then performed to achieve the target enhancement effect: ； ； wherein, is the band number of the optical band, N is the number of optical bands of the remote sensing image, is the background seawater difference of the band, and are respectively the maximum and minimum values in the band difference spectrum curve, and finally the band normalized difference reflectance data is obtained.

6. The method for sea ice remote sensing recognition based on ultraviolet reflection difference according to claim 1, characterized in that: In step 5, threshold ​ 1 = 0.05, threshold ​ 2 = 0.4, threshold ​ 3 = 0.5, threshold ​ 4 = 266.5K, threshold ​ 5 =0.

75.

7. The method according to claim 3, wherein the method is characterized by: ​ 8. The method for sea ice remote sensing recognition based on ultraviolet reflection difference according to claim 2, characterized in that: In step 6, sea ice concentration SIC and sea ice cover area S ice are calculated using the red light band, as follows: ； ； wherein, is the number of the pixel, is the sea ice concentration of the pixel, is the apparent reflectance threshold of the sea water pixel ​ 5, is the apparent reflectance value of the pure sea ice pixel, which is obtained by counting the highest frequency of the apparent reflectance value in the sea ice pixel, is the area of the image pixel, is the sea ice coverage area in the pixel.​ 9. The sea ice remote sensing identification method based on ultraviolet reflection difference according to claim 8, characterized in that: ​ 5 =0.05。

Images